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Robert E. Tuleya and Stephen J. Lord


The National Centers for Environmental Prediction (NCEP) and the Hurricane Research Division (HRD) of NOAA have collaborated to postprocess Omega dropwindsonde (ODW) data into the NCEP operational global analysis system for a series of 14 cases of Atlantic hurricanes (or tropical storms) from 1982 to 1989. Objective analyses were constructed with and without ingested ODW data by the NCEP operational global system. These analyses were then used as initial conditions by the Geophysical Fluid Dynamics Laboratory (GFDL) high-resolution regional forecast model.

This series of 14 experiments with and without ODWs indicated the positive impacts of ODWs on track forecasts using the GFDL model. The mean forecast track improvement at various forecast periods ranged from 12% to 30% relative to control cases without ODWs: approximately the same magnitude as those of the NCEP global model and higher than those of the VICBAR barotropic model for the same 14 cases. Mean track errors were reduced by 12 km at 12 h, by ∼50 km for 24–60 h, and by 127 km at 72 h (nine cases). Track improvements were realized with ODWs at ∼75% of the verifying times for the entire 14-case ensemble.

With the improved analysis using ODWs, the GFDL model was able to forecast the interaction of Hurricane Floyd (1987) with an approaching midlatitude trough and the storm’s associated movement from the western Caribbean north, then northeastward from the Gulf of Mexico into the Atlantic east of Florida. In addition, the GFDL model with ODWs accurately forecasted the rapid approach and landfall of Hurricane Hugo (1989) onto the U.S. mainland. An assessment of the differences between analyses indicates that the impact of ODWs can be attributable in part to differences of ∼1 m s−1 in steering flow of the initial state.

In addition to track error, the skill of intensity prediction using the ODW dataset was also investigated. Results indicate a positive impact on intensity forecasts with ODW analyses. However, the overall skill relative to the National Hurricane Center statistical model SHIFOR is shown only after 2 or 3 days. It is speculated that with increased data coverage such as ODWs both track and intensity error can be further reduced provided that data sampling can be optimized and objective analysis techniques utilizing asynoptic data can be developed and improved.

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John C. Derber, David F. Parrish, and Stephen J. Lord


At the National Meteorological Center (NMC), a new analysis system was implemented into the operational Global Data Assimilation System on 25 June 1991. This analysis system is referred to as Spectral Statistical Interpolation (SSI) because the spectral coefficients used in the NMC spectral model are analyzed directly using the same basic equations as statistical (optimum) interpolation. The major differences between the SSI analysis system and the conventional optimum interpolation (OI) analysis system previously used operationally at NMC are:

  • –The analysis variables are closely related to the coefficients of the NMC spectral model.

  • –Temperature observations are used, not heights as in the previous procedure. As a result, aircraft temperatures are being used for the first time at NMC.

  • –Nonstandard observations, such as satellite estimates of total precipitable water and ocean-surface wind speeds, can be easily included.

  • –No data selection is necessary. All observations are used simultaneously.

  • –The dynamical constraint between the wind and mass fields is more realistic and applied globally.

  • –Model initialization has been eliminated. The analysis is used directly as the forecast model initial condition.

Extensive pre-implementation testing demonstrated that the SSI consistently produced superior analyses and forecasts when compared to the previous OI system. Improvement in skill is shown not only for the 3–5-day forecasts, but also in one-day aviation forecasts.

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Stephen J. Lord, Xingren Wu, Vijay Tallapragada, and F. M. Ralph

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical precipitation forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the NCEP Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble-variational (4DEnVar) data assimilation system. The control run (CTRL) used all the routinely assimilated data and included ARR dropsonde data, whereas the denial run (DENY) excluded the dropsonde data. There were 17 Intensive Observing Periods (IOPs) totaling 46 Air Force C-130 and 16 NOAA G-IV missions to deploy dropsondes over targeted regions with potential for downstream high-impact weather associated with the ARs. Data from a total of 628 dropsondes were assimilated in the CTRL. The dropsonde data impact on precipitation forecasts over U.S. West Coast domains is largely positive, especially for day 5 lead time, and appears driven by different model variables on a case-by-case basis. These results suggest that data gaps associated with ARs can be addressed with targeted ARR field campaigns providing vital observations needed for improving U.S. West Coast precipitation forecasts.

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